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新论文 · v1arXiv:2611.06984

Resource-Efficient Reinforcement Learning Post-Training through Elastic Expert Parallelism

Jia Wei, Ziyi Qiu, Yi Xu, Xuehai Qian

原文摘要

Reinforcement learning post-training improves the capabilities of large language models, but generating training responses is a major computational bottleneck. In mixture-of-experts models, workers that finish their assigned responses may remain occupied because their experts are still needed by workers processing longer responses. Consequently, approximately one-third of occupied worker time in the baseline occurs after local response generation finishes. Here we present FlexFloor, a system that enables earlier resource release during synchronous response generation while preserving the expert computation required by ongoing responses. FlexFloor jointly adapts response scheduling and the active group of workers. It uses historical response lengths and memory constraints to guide batching and prompt placement, then progressively releases workers while maintaining expert availability and correct communication. Compared with the verl baseline, FlexFloor generates approximately 65.40% more response tokens per unit of occupied computing time and increases response generation throughput by 52.74%, with little observed variation in rollout reward. By making reinforcement learning post-training more efficient, FlexFloor supports faster development and iterative improvement of large language models and AI agents.

cs.AI · cs.DC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06660

Perceived Trust and Transparency in AI-Altered vs. Human-Written LinkedIn Posts

Thomas Christian Joos, Azza Bouleimen, Anikó Hannák, Nicolò Pagan

原文摘要

People increasingly draft a social media post themselves and then let an AI model refine it (AI-altered), a common but understudied middle case between fully human-written and fully AI-generated content. We ask whether this changes how trustworthy and transparent readers perceive the post to be, and whether they can even tell. We first analyze real ChatGPT conversations (WildChat) to find how people actually alter social media posts, which we use to design realistic alteration prompts. We then run a pre-registered, within-subjects online experiment (n=214) in which participants rate unlabeled human-written and AI-altered LinkedIn-style posts for perceived trustworthiness and transparency, and separately judge whether each post was AI-altered. We find that AI-alteration itself has no detectable effect on either perceived trustworthiness or transparency (both estimates near zero, not significant). At the same time, readers did not reliably detect AI-alteration (49\% accuracy, near chance) and defaulted to assuming human authorship when unsure. However, our analysis shows significant differences in transparency and trustworthiness ratings across the specific post, the AI model, as well as the prompt used to alter it. For instance, more detailed prompts raise both perceived trustworthiness and transparency but also make the alteration more detectable. Surprisingly, the prompt that shifted ratings most also asked the model to add one new idea absent from the original post: readers nonetheless rated this AI-invented, unverifiable content as more trustworthy and transparent.

cs.HC

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06658

The determinantal complexity of the 4*4 permanent is 15

Karthik Sheshadri

原文摘要

The best known lower bound on the determinantal complexity of the 4x4 permanent is 10. In this short note, I prove that this value is exactly 15: the 4x4 permanent is the determinant of a 15x15 matrix of affine linear forms, and cannot be that of any smaller matrix. To do so, I must establish both a lower and upper bound. To establish the upper bound, I use Grenet's construction of size 2^n-1. Hence that construction is optimal at n=4 (and n=3). This now constitutes the first knwon exact value of the determinantal complexity of the permanent at n=4. Only the value at n=3 was exactly known, prior to this note. The general quadratic bound only gives 8 for n=4. To establish the lower bound, I work from a normal form at a zero of the permanent. In the first row (and column) of the normal form, the linear forms vanish on a linear space inside the singular locus of the permanent. A hessian inequality bounds the size of the matrix in terms of that space. Then, a matrix of sie 14 foces the space into any of nine corordinate types. The forcing is either up to symmetry, or else into a family that degenerates to one of them. I then rule out every case. All involved computations are exact. In respect of the claim, I have made all certificates public.

cs.CC

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arXiv ↗PDF ↗
版本更新 · v3arXiv:2602.04093

Federated Concept-Based Models: Interpretable models with distributed supervision

Dario Fenoglio, Arianna Casanova, Francesco De Santis, Gabriele Dominici, Johannes Schneider, Pietro Barbiero, Giovanni De Felice, Marc Langheinrich 等 9 位作者

原文摘要

Concept-based Models (CMs) enhance interpretability in deep learning by grounding predictions in human-understandable concepts. However, concept annotations are costly and rarely available at scale within a single data source. Federated Learning (FL) could alleviate this limitation by enabling cross-institutional training over concept annotations distributed across multiple data owners. Yet, FL lacks interpretable modeling paradigms. Integrating CMs with FL is non-trivial: although FL supports heterogeneous and non-stationary client participation, it typically assumes a fixed shared architecture, whereas CMs may require architectural adaptation as the available concept set evolves. We propose Federated Concept-based Models (F-CMs), a new methodology for deploying CMs in evolving FL settings. F-CMs aggregate concept-level information across institutions and efficiently adapt the model architecture to changes in concept supervision while preserving privacy. Empirically, F-CMs maintain accuracy and intervention effectiveness comparable to training settings with full concept supervision, while outperforming on average non-adaptive federated baselines. Notably, F-CMs enable interpretable inference on concepts unavailable to a given institution, a key novelty over existing approaches.

cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06657

Approximating #BIS in $\exp(\widetilde O(n^{1/3}))$ Time

Jingcheng Liu, Yitong Yin

原文摘要

We give randomized algorithms for approximately counting and nearly uniformly sampling independent sets in arbitrary bipartite graphs (#BIS). At fixed accuracy, both algorithms run in $\exp(O(n^{1/3}\log^{4/3}n))$ time on $n$-vertex graphs. More generally, the same running-time dependence on the number of variables holds for approximate counting and sampling with efficiently evaluable positive log-supermodular weights on Boolean variables. A weighted Sinclair-Jerrum walk on the self-reduction tree reduces these tasks to coarse lower estimates of residual log-partition functions. We obtain these estimates by optimizing a concave relaxation of an exact variational principle on the tree; threshold smoothing certifies that the low-degree search suffices.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.40902

FlapSim: A Composable MuJoCo Simulation Platform and Reduced-Order Aerodynamic Model Hierarchy for Flapping-Wing Robotics

Mingshan He

原文摘要

Flapping-wing simulation need to reconcile high-frequency wingbeat aerodynamics with the slower dynamics relevant to flight control and reinforcement learning. We present FlapSim, a modular open-source MuJoCo simulation platform that combines robot descriptions, wing geometry, and composable aerodynamic models in a deterministic simulation pipeline. Across $48$ held-out scenarios spanning frequency, gimbal, wind, frequency-step, gust, and cross-robot flat-plate regimes, we compare five reduced-order configurations operating at $1\,\text{ms}$ with a $50\,μ\text{s}$ quasi-steady blade-element reference. The built-in hover cycle-averaged baseline achieves a $67.51\times$ CPU speedup but only $0.6593$ mean wrench fidelity. Combining multirate $21$-substep clock synchronization, three-dimensional linear/quadratic inflow and gust coupling, analytical blade-element geometry scaling, and phase-locked $1f\text{--}6f$ harmonics raises mean fidelity to $0.9646$ at a $65.19\times$ speedup in a five-block model. A streamlined four-block model without explicit step-transient lag states achieves $0.9662$ fidelity at a $67.98\times$ speedup. Controlled ablations show that stroke harmonics are particularly important during frequency transitions, improving held-out frequency-step fidelity by $+0.3176$. Transfer-function analysis explains this effect: fixed-window cycle averaging retains harmonic contributions when the wingbeat frequency changes, so within-cycle reconstruction also improves the filtered wrench. These results characterize the accuracy--computation tradeoff of the aerodynamic hierarchy and identify timing, inflow, geometry, and harmonic representation as central considerations for reduced-order flapping-wing simulation. The source code are available at: https://mingshanhe.github.io/FlapSim/\href{https://mingshanhe.github.io/FlapSim/}.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2609.26617

MMAP: Multimodal Missing-Aware Pretraining for Longitudinal Alzheimer's Prediction

Fiona Kekwick, Matthew Baugh, Bernhard Kainz, Paul M. Matthews, Wenjia Bai

原文摘要

Clinical decision making heavily relies on predicting the disease progression trajectory by seeking to understand patient's health status which is characterised by multimodal medical data. AI holds great potential for learning useful representations from multimodal medical data to predict disease progression and aid clinical decision making. However, development of predictive AI models is constrained by missing modalities and incomplete tabular data frequently occurring in medical datasets. In addition, disease labels alone may only provide limited supervisory signals for learning representations from high-dimensional multimodal data. Here, we present MMAP, a novel Multimodal Missing-aware Alignment Pretraining method for learning image-tabular representations from incomplete data. An image encoder is pretrained with efficient sigmoid contrastive learning combined with generative reconstruction. A tabular encoder is built upon a tabular foundation model. A missing token generator enables the two encoders to take incomplete data as input, enabling the model to be robust against missing modalities, either with missing images or missing tabular data. We evaluate the clinical usefulness of the learnt multimodal representations on two challenging longitudinal clinical tasks for Alzheimer's disease: predicting disease stage conversion and predicting amyloid status. The proposed method outperforms strong multimodal and unimodal baselines.

cs.CV · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06655

Beyond accuracy: what AI assurance needs to establish in public services

Louise Yorke

原文摘要

Public bodies increasingly rely on AI-produced evidence, including classifications, summaries and assessments. These outputs can inform policy decisions and service delivery, making their reliability a matter of public accountability. Establishing whether this evidence can be relied upon requires testing its outputs and examining how organisations decide whether those tests are sufficient. We combine technical evaluation with analysis of AI governance requirements, informed by measurement theory and management-based regulation. We compare eight GPT-4o configurations classifying 1,200 UK Prime Minister's Questions into 21 policy topics, against human-coded labels and across unchanged repeats. Accuracy ranges from 68.2% to 70.3%, yet 24.3% of questions receive different labels, with changes in topic shares and rankings. Configurations agree more with one another than with human coding: agreement does not guarantee correctness. Under the implemented score-extraction procedure, the proportion flagged for human review ranges from 0.9% to 41.0%. We analyse 37 UK and EU policy documents. Both sets expect testing and oversight, but reporting expectations do not consistently cover changing answers, shared errors, selective evaluation, substantive conclusions or review routing. Some UK guidance explicitly addresses stability. The UK sample emphasises public bodies checking their own use; the EU places substantial testing duties on providers while leaving them important choices about measures and acceptance thresholds. Without appropriate checks, evidence could misrepresent policy issues, redirect attention or resources, and leave errors undetected. Public policy should require organisations to justify reliance through checks beyond aggregate accuracy, clear responsibility, access to expertise and scrutiny of results.

cs.CY · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06654

Compounding Physical RSI: Turning Execution Failure into Inherited Capability

Yixuan Yang, Qiang Chen, Run Shao, Shuwei Dong, Wenhao Li, Yunpeng Wang, Wenhan Fang, Haomiao Wu 等 34 位作者

原文摘要

Generalist robot policies are trained on static offline datasets, yet physical manipulation unfolds sequentially across stages. Because a weakness in a downstream stage remains unobservable until the policy passes earlier ones, each resolved bottleneck exposes the next hidden failure mode. This staged structure calls for Recursive Self-Improvement (RSI), in which a policy iteratively executes, diagnoses failures, and updates itself. Moreover, because task completion behaves like a product of stage-level pass rates, recursive gains can multiply across generations. In physical environments, three bottlenecks obstruct compounding: terminal execution feedback rarely localizes the stage that caused a failure, failed trajectories do not supply the corrective action, and updating shared parameters can degrade previously mastered skills. We propose Compounding Physical RSI, a self-evolving policy framework that turns execution failures into heritable capability. First, automated capability-gap assessment traces failed rollouts to the task stage where progression breaks down and formulates a structured improvement objective. Second, conditioned on this objective, our remedy stage curates validated, stage-relevant experience and refines the policy via behavior-regularized offline reinforcement learning, without requiring costly online rollouts at each optimization step. Third, to safeguard existing skills, our retention stage constructs weight-space candidates between the inherited reference policy and the refined policy, evaluates them across the task suite, and promotes the best candidate as the reference state for the next generation. Evaluated across all 42 tasks of the RoboDojo-Sim benchmark, CSU-AI-07 ranks first on the leaderboard with a three-seed mean Overall Score of 26.15 and Success Rate of 20.70%, outperforming the next-best entry by 5.92 points and 5.82 percentage points.

cs.RO

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06653

Detecting Silent Action-Interface Mismatch with Calibrated Relational World Models

Rui Zhu, Xurui Li, Haixu Tang

原文摘要

A robot action can satisfy shape and range constraints yet carry the wrong semantics: axes may be permuted, signs inverted, rotations rescaled, or gripper polarity reversed. We study learned state-action-effect relations as offline contracts for detecting these mismatches and ranking their likely fields. Clean held-out transitions calibrate alarms; a fixed inverse vocabulary supports closed-set localization. On ten LIBERO Spatial tasks comprising 432 source episodes, a capacity-controlled comparison at 2.5% empirical clean false-positive rate gives 46.1% recall for a ridge forward residual, 61.0% for a four-component joint Gaussian mixture, and 65.7% for the exact conditional of that same mixture. The joint score retains a 14.9 percentage-point advantage over ridge, while conditioning improves recall by a further 4.7 points. Joint scoring instead has higher localization: 95.8% versus 94.6%. These matched-rate comparisons are retrospective diagnostics, not deployment calibration guarantees. Earlier Object/Goal results broaden tasks within the LIBERO convention; DROID, all three HYDRA tasks, short temporal faults, small offsets, exact repair, and simulated mitigation fail their respective gates, while xArm contributes one task only. The evidence supports a bounded offline diagnostic and a detection-localization tradeoff, rather than joint-density superiority or cross-robot safety.

cs.RO · cs.LG

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06652

Dynamical State-Space Diffusion for Spatiotemporal Prediction

Woojun Lee, Hankyeol Kim, Pilsung Kang

原文摘要

Diffusion models for temporal prediction typically corrupt each future frame with independent noise, leaving temporal dependence entirely to the denoiser. We propose Dynamical State-Space Diffusion (DSSD), which learns the temporal structure of the corruption process itself. DSSD builds a temporal kernel from the damped oscillatory modes of a stable diagonal state-space model, whose learned decay rates and frequencies let the corruption capture multiple timescales and oscillatory correlations, while the denoiser architecture remains unchanged. Running this model on a finite clip, however, requires the state before the first frame, which is unobserved; starting from a zero state leaves the early frames with less noise than the diffusion schedule prescribes. We avoid this by using the Cholesky factor of the model's stationary covariance as the temporal operator. We prove that this operator is causal, exactly invertible, keeps each frame's noise variance equal to that of standard diffusion, and reduces to the identity at the clean end of the process, and that it is exactly the Kalman innovations form of the state-space model. The denoiser is trained to predict the temporally correlated noise, and sampling removes and reapplies the temporal mixing at each step. On scientific-field and robot-video datasets, DSSD outperforms diffusion baselines that share its denoiser, and the evolution of its learned modes during training shows that the temporal operator adapts to each dataset.

cs.CV

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06649

SCRUB: Contact-Based Surface-Frame Estimation for Force-Motion Control with an Armless Quadruped

Sourav Selvaraj, Juan Pablo Espinosa Chessal, Mahdi Agheli

原文摘要

Wiping, sanding, and inspection robots press a sliding tool on a surface with controlled force. Controlling that force needs the surface normal, which is unknown on real-world, unmapped surfaces. Surface-normal estimators can help estimate the surface normal, but they either need a friction model or assume that the instantaneous tool velocity lies along the surface; however, walking violates this assumption. We propose SCRUB, a contact-based surface-frame estimator, which lets an armless quadruped slide a body-mounted tool along near-vertical surfaces in the absence of known, axis-aligned, or vision-predicted normals. SCRUB's predictive controller presses and slides the tool using a Kalman-filter estimate of the normal from two cues: 1) A force cue measures the estimate's tilt across the motion. If friction directly opposes the motion, the cue is zero exactly when that tilt is zero, for any friction coefficient. However, if the cue's sliding direction is wrong, friction appears as a false tilt. 2) A velocity cue measures tilt along the motion. Walking and pressing move the tool in and out. SCRUB therefore subtracts the commanded press rate from the tool velocity, averages the result over each gait cycle, and treats this mean as lying along the surface. On Unitree Go1 hardware at nominal speed and load, SCRUB completed every wall traverse and column orbit. No orbit completed with either cue removed, without gait averaging, or with a fixed normal.

cs.RO

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2610.20590

Feed-Forward Multi-View Feature Coding for 3D Gaussian Splatting

Seungmi Choi, Ji-Hyoun Choi, Suhyun Jo, Tae-Gwon Park, Hui Yong Kim

原文摘要

Feed-forward 3D Gaussian Splatting (3DGS) methods enable efficient reconstruction of 3D scenes from sparse multi-view images in a single forward pass, without scene-specific optimization. However, existing feed-forward scene-coding pipelines follow a "generate-then-compress" framework, first generating a Gaussian representation and then encoding its geometry and appearance through separate coding paths, complicating codec design and limiting the exploitation of cross-attribute redundancy. We propose multi-view feature coding for Gaussian splatting (MFGS), an end-to-end feed-forward framework for scene coding. Instead of compressing an explicit Gaussian representation, MFGS extracts multi-view features from the input images and encodes them into a compact bitstream. Through end-to-end rate-distortion training, MFGS learns a compact latent representation of multi-view features that jointly represents geometry and appearance, enabling the receiver to predict Gaussian attributes from the decoded features. We further introduce importance-guided Gaussian selection to accommodate different rendering budgets, allowing the receiver to adjust the number of Gaussians used for rendering from the same bitstream. Across RE10K, ACID, and DL3DV, MFGS achieves 99.76-99.86% bitrate savings over the generate-then-compress pipeline, as measured by BD-rate, while reducing total GMACs by 52-55% and providing an approximately 12 times end-to-end speedup.

cs.CV

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2510.27428

Learning Soft Robotic Dynamics with Active Exploration

Hehui Zheng, Bhavya Sukhija, Chenhao Li, Klemens Iten, Andreas Krause, Robert K. Katzschmann

原文摘要

Soft robots offer adaptability and safety in unstructured environments, yet their compliant, high-dimensional, and nonlinear dynamics make modeling for control difficult. Existing data-driven approaches often fail to generalize, constrained by narrowly focused task demonstrations or inefficient random exploration. We introduce an uncertainty-aware active exploration framework that autonomously learns task-agnostic and generalizable dynamics models of soft robotic systems. We evaluate this framework on three simulated platforms---a continuum arm, an articulated fish in fluid, and a musculoskeletal leg---and on a real-world pneumatically actuated continuum arm. Compared with random exploration and task-specific baseline, our method achieves higher modeling accuracy by actively collecting informative data. This improved model quality yields higher returns in zero-shot control on unseen tasks and robust performance under sensing noise, actuation delays, and material nonlinearities. These results demonstrate that uncertainty-driven active exploration enables data-efficient dynamics learning across soft robotic morphologies, supporting autonomous and adaptable control for compliant robots.

cs.RO · cs.AI

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2508.19008

Computational Phenomenology of Borderline Personality Disorder: A Comparative Evaluation of LLM-Simulated Expert Personas and Human Clinical Experts

Marcin Moskalewicz, Anna Sterna, Karolina Drożdż, Kacper Dudzic, Marek Pokropski, Paula Flores

原文摘要

Building on a human-led thematic analysis of clinical life-story interviews (> 150,000 words) with inpatients with Borderline Personality Disorder, this study examines the capacity of large language models (OpenAI's GPT, Google's Gemini, and Anthropic's Claude) to support qualitative clinical analysis. The models' interpretative potential was evaluated using a mixed-methods approach. Study A involved blinded and non-blinded judges in phenomenology and clinical psychology. The experts assessed the validity of AI-generated content using semantic congruence, Jaccard coefficients, and multidimensional validity ratings, including credibility, coherence, the substantiveness of results, and grounding in qualitative data. In Study B, neural methods were used to embed human- and model-generated theme descriptions in a multidimensional vector space. This approach provided an objectified computational measure of the difference between human and model semantics and linguistic style. In Study C, complementary non-expert evaluations were conducted (N=115) to examine the influence of thematic verbosity on the perception of human authorship and content validity. Overall, the results of AI analysis showed a highly variable overlap (0-58%) with the human interpretation, while all models identified themes originally omitted by human researchers, proving their capacity to mitigate human bias. At the thematic level, external evaluators were unable to reliably distinguish human-authored themes from those generated by AI. In terms of content validity assessed against raw data by high-level experts in the blinded mode, the performance of Gemini 2.5 Pro was indistinguishable from that of humans. The comparison of semantic vector embeddings showed that its style was also the closest to humans.

cs.AI · cs.CL

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06642

Tight bounds for fractional and randomized truthful scheduling

Georgios Anastasiadis, George Christodoulou, Elias Koutsoupias, Annamaria Kovacs, Conrad Schecker

原文摘要

We resolve one of the longest-standing open problems in algorithmic mechanism design by showing that randomization cannot yield a sublinear approximation for truthful scheduling on unrelated machines, even when truthfulness is required only in expectation. Our main technical result concerns fractional scheduling, where tasks may be split among machines. We prove that every deterministic truthful fractional mechanism on $n$ unrelated machines has worst-case approximation ratio at least $(n+1)/2$, even against the optimal integral makespan. This matches the known upper bound exactly and settles the optimal approximation ratio of truthful fractional scheduling. Since the expected allocations and payments of a truthful-in-expectation mechanism define a truthful fractional mechanism, the same lower bound applies to all truthful-in-expectation mechanisms and, in particular, to all universally truthful mechanisms. On the positive side, we give a universally truthful mechanism with approximation ratio $n/2+o(n)$. Together, these results establish the optimal leading constant $1/2$ for randomized truthful scheduling. Thus, randomization improves on the optimal deterministic approximation ratio by an asymptotic factor of two, and no more.

cs.GT

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arXiv ↗PDF ↗
版本更新 · v2arXiv:2607.07934

Homomorphism Indistinguishability Beyond Graphs: Relational Weisfeiler--Leman and Hypertree Width

Panagiotis Aivasiliotis, Hubie Chen, Andreas Göbel, Matthias Lanzinger, Marc Roth, Markus Schneider

原文摘要

The Weisfeiler--Leman (WL) algorithm is one of the most influential heuristics for the graph isomorphism problem. The expressive power of WL has been extensively studied in the contexts of descriptive complexity, logics, graph neural networks, and the theory of homomorphism indistinguishabily. Notably, two graphs are indistinguishable by the $k$-dimensional WL algorithm if and only if they are indistinguishable by homomorphism-counts from graphs of treewidth at most $k$. An intrinsic question is to find a natural version of the WL algorithm for relational structures of higher arity admitting an equivalent characterisation via homomorphism indistinguishability along bounded generalised hypertree width (GHW). Scheidt and Schweikardt solved this for $k=1$ by defining the RCR algorithm and showing indistinguishability from $α$-acyclic structures. In this work, we resolve this for all $k\ge1$: we develop a $k$-dimensional version of RCR ($k$-RCR for short) and show that two structures $\mathcal A$ and $\mathcal B$ are indistinguishable by $k$-RCR if and only if they have the same number of homomorphisms from all structures $\mathcal C$ of generalised hypertreewidth at most $k$. Furthermore, we show that on a structure $\mathcal A$ over a fixed signature, with $\mathsf{ghw}(\mathcal{A})\leq k$, $k$-RCR runs in $O(\|\mathcal A\|^k\cdot\log(\|\mathcal A\|))$, where $\|\mathcal A\|$ is the number of all tuples that appear in the relations of $\mathcal A$.

cs.DS

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arXiv ↗PDF ↗
版本更新 · v4arXiv:2602.20207

Golden Layers and Where to Find Them: Improved Knowledge Editing for Large Language Models Via Layer Gradient Analysis

Shrestha Datta, Hongfu Liu, Anshuman Chhabra

原文摘要

Knowledge editing in Large Language Models (LLMs) aims to update the model's prediction for a specific query to a desired target while preserving its behavior on all other inputs. This process typically involves two stages: identifying the layer to edit and performing the parameter update. Intuitively, different queries may localize knowledge at different depths of the model, resulting in different sample-wise editing performance for a fixed editing layer. In this work, we hypothesize the existence of fixed golden layers that can achieve near-optimal editing performance similar to sample-wise optimal layers. To validate this hypothesis, we provide empirical evidence by comparing golden layers against ground-truth sample-wise optimal layers. Furthermore, we show that golden layers can be reliably identified using a proxy dataset and generalize effectively to unseen test set queries across datasets. Finally, we propose a novel method, namely Layer Gradient Analysis (LGA) that estimates golden layers efficiently via gradient-attribution, avoiding extensive trial-and-error across multiple editing runs. Extensive experiments on several benchmark datasets demonstrate the effectiveness and robustness of our LGA approach across different LLM types and various knowledge editing methods.

cs.LG · cs.AI

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06641

The Logic of Emergence

David H. Wolpert, David C. Krakauer

原文摘要

``Emergence'' refers to the fact that different mathematical languages and theories seem to be required to describe scientific theories that operate at different spatial and temporal scales. Highly circumscribed, special cases of emergence have been investigated using many different formal tools, including coarse-graining (over space and / or time), renormalization, dimension reduction, broken symmetry, and various formulations of causality. Here we extend the mathematics of model theory to unify and extend these earlier investigations. As a preliminary application of our framework, we investigate canonical examples of emergence, including the emergence of genetics from chemistry, of psychology from neurobiology, and of different forms of collective behavior from the laws governing their constituent parts. By exploiting the power of model theory we also uncover many aspects of emergence that were previously obscured. In particular, we uncover some of the rich hierarchical structure that relates different scientific fields. For example, we show that scientific fields can be related via a collection of multiple partial orders, all defined over the same set. On the other hand, under an alternative relation among scientific fields, one which allows cycles, we show that it is possible that the set of all of scientific fields must collapse into a single scientific field. Going forward, these investigations point to the formal tools that might need to be developed in order to arrive at a deeper understanding of this emergent hierarchy of scientific reality.

math.LO · cs.LO · physics.hist-ph

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06639

Replicability, Reproducibility, and Robustness in Political and Social Science Research with Large Language Models

Joan C. Timoneda, Sebastián Vallejo Vera

原文摘要

This chapter addresses the central reproducibility problem of research with large language models. LLM output is probabilistic and varies across models and versions, which can have significant effects on downstream statistical results. The chapter distinguishes between replicability, reproducibility, and robustness, and proposes a probabilistic standard based on the distribution of results, not identical outputs. It introduces a standardized prompt script, tests within-model variation for encoders and decoders, and develops error-structure analysis to diagnose where models err and whether their errors align. Using three datasets, the chapter shows that measurement choice can reverse the sign of downstream regression coefficients. Existing design-based corrections with gold-labeled subsamples restore validity, but come at the cost of estimate precision. The main contribution of the chapter is a stacked ensemble of open-weight models that improves precision in the correction step while retaining estimate validity.

cs.CL · cs.CY

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arXiv ↗PDF ↗
新论文 · v1arXiv:2611.06637

Human Motion Prediction via Continual Prior Compensation

Jianwei Tang, Jian-Fang Hu, Tianming Liang, Xiaotong Lin, Jiangxin Sun, Wei-Shi Zheng, Jianhuang Lai

原文摘要

Human Motion Prediction (HMP) aims to predict future human poses at different moments according to observed past motion sequences. Previous approaches mainly treated the prediction of different temporal moments as a single prediction task and learned the predictions of varied moments simultaneously, which would encounter a main limitation: the learning of short-term predictions (referring to ``near-future'' prediction) could be hindered by the predictions of long-term (referring to ``far-future'' prediction) motions. In this paper, we develop a novel temporal continual learning framework called Continual Prior Compensation (CPC) to progressively train HMP models, in which we divide the prediction task of motions corresponding to varied temporal moments into several subtasks and train the model in a multi-stage manner. To mitigate the prior information forgetting in the progressive training, we further introduce a learnable random variable Prior Compensation Factor (PCF) to explicitly measure the prior knowledge loss. We theoretically show that the PCF can be efficiently learned together with the model parameters by minimizing a reasonable upper bound of the objective function. The proposed CPC is further enhanced to estimate the prior information loss for each subtask and a new framework called Continual Prior Compensation++ (CPC++) with Fine-Grained Prior Compensation Factor (FGPCF) is finally developed. Our CPC and CPC++ frameworks are quite flexible and can be easily integrated with different HMP backbone models and adapted to various datasets and applications. Extensive experiments on three HMP benchmark datasets using multiple SOTA HMP backbones (PGBIG, siMLPe, MotionMixer, and LTD) demonstrate the effectiveness and flexibility of our frameworks.

cs.CV · cs.AI

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新论文 · v1arXiv:2611.06636

Non-induced inherent graphs

Vladimir Gurvich, Matjaž Krnc, Martin Milanič, Mikhail Vyalyi

原文摘要

Concepts of avoidable paths and inherent two-rooted graphs were recently introduced for the case of induced subgraphs. In this paper, we translate this framework to the setting of not necessarily induced (NNI) subgraphs, where the structural constraints, as well as conclusions, are significantly relaxed. We develop a new sufficient condition for inherence, and use it to provide complete characterizations of inherent two-rooted paths and forks. We show that for these families, the obvious necessary condition for inherence from the induced case is sufficient in the NNI setting. To complement these results, we introduce two general methods for proving non-inherence and apply them to demonstrate the non-inherence of several classes of two-rooted trees. Keywords: subgraph, two-rooted graph, two-rooted tree, avoidable two-rooted graph, inherent two-rooted graph

math.CO · cs.DM

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新论文 · v1arXiv:2611.06635

Uncertainty-Driven Efficient Communication for UAV-Based Collaborative Perception

Jiuwu Hao, Yuting Wan, Liguo Sun, Zao Zhang, Yueyang Wu, Pin Lv

原文摘要

Unmanned aerial vehicle (UAV)-based collaborative perception enables multiple agents to share complementary perceptual information, overcoming limitations such as occlusion and limited sensing range. Communication efficiency is a critical bottleneck, as UAVs operate under stringent bandwidth and computational constraints. However, existing spatial selection methods focus predominantly on foreground regions to reduce missed detections, neglecting perceptually ambiguous regions and leaving false detections unresolved. We propose UCP, an uncertainty-driven framework that characterizes collaborative value through observational uncertainty, identifying sensor-limited regions, and discriminative uncertainty, capturing model-level ambiguity near the decision boundary. These estimates drive spatial request and supply maps for efficient inter-agent communication, and the received messages are aggregated via U-Fusion, an uncertainty-guided cross-attention module. Experiments on three benchmarks across 3D object detection and trajectory prediction demonstrate a superior performance-bandwidth trade-off with high interpretability and strong generalizability.

cs.CV

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新论文 · v1arXiv:2611.06634

flash-comm: Self-Synchronizing Collective Communication for LLM Decoding

Carrie Wang, Jack Li, Jason Zhao

原文摘要

We present flash-comm, a communication library that achieves efficient transport for LLM decoding by fusing readiness synchronization with data movement. flash-comm introduces self-synchronizing transfers in which each data block carries evidence of its readiness, allowing receivers to directly validate the blocks they need without separate inter-GPU barriers. Based on this principle, a cost-guided protocol selector adapts the readiness encoding to message size and the number of participating GPUs, balancing synchronization overhead against additional data-transfer cost. flash-comm further decouples transport order from reduction order to preserve result consistency across GPUs under asynchronous data arrivals. On a server with eight NVIDIA B300 GPUs, flash-comm reduces all-reduce latency by a median of 28.8% and up to 43.7% compared with custom AR v2 (the state-of-the-art all-reduce implementation). In end-to-end decoding, flash-comm reduces time per output token (TPOT) by 1.7-6.8%. Note that all-reduce accounts for 11.4% of the baseline decoding time, which bounds the latency reduction possible from optimizing this operation alone. The same mechanism also supports standalone all-gather, reducing latency by a median of 23.8% and up to 58.9% across eight B300 GPUs.

cs.DC

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新论文 · v1arXiv:2611.06982

Decode Before You Hear: Speculative Listening for Chunk-Interleaved Full-Duplex Speech Language Models

Yutong Bian, Yanglin Zhang, Yuxiang Wang, Luoyuan Zhang, Rongshen He, Zihao Wang, Junwen Qiu, Jiaqi Li 等 9 位作者

原文摘要

Chunk-interleaved full-duplex speech language models listen and speak in fixed time chunks, and each chunk must be processed before the next one arrives. Their workload is asymmetric across chunks: listen chunks leave compute idle while awaiting audio, and speak and tool-call chunks require serial generation, which delays responses and tool calls. Using the idle compute for the next chunk requires decoding it before its audio arrives, under an assumed input. Taking silence as the assumed input and comparing the output with the output under real audio on four models, we observe two properties. First, the next audio chunk does not change the output in 95.3% of chunks. Second, 88% of changes begin at the first output token. Building on these properties, we propose Speculative Listening, a training-free, lossless framework for input-time self-speculative decoding. During idle time, the model drafts the next chunk under silence and, for changes at the first token, a second branch from the runner-up first token. It keeps all drafts in a cross-chunk hypothesis pool and verifies them when the audio arrives. On our eight-task benchmark TickTock-Bench, Speculative Listening speeds up speak chunks by 2.6-2.8x and tool-call chunks by up to 23.4x. On average, it reduces the decoding delay before a response starts by 73%, to 74-241 ms, close to the 200 ms gap between human turns, and the maximum lag behind real time by 94.4%.

cs.SD

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新论文 · v1arXiv:2611.06633

TestBED: An Open-Source Toolkit for Bayesian Experimental Design

Gavin Kerrigan, Marcel Hedman, Sahel Iqbal, Tom Rainforth

原文摘要

Bayesian experimental design (BED) is a principled framework for intelligent data acquisition with applications across a wide range of scientific and industrial domains. However, the development and adoption of BED remains hindered by a lack of standardised software and benchmarks. Existing methods are typically implemented in bespoke codebases and evaluated on a small subset of tasks, making methods difficult to apply, compare, or extend. To address this, we present TestBED, a general-purpose, open-source toolkit for Bayesian experimental design. TestBED decomposes the BED process into modular, reusable components spanning tasks, objectives, training mechanisms, policy architectures, and evaluation procedures, together with scalable implementations of core primitives such as expected information gain estimators. We provide a wide range of these components, which can be freely composed, allowing existing methods to be reproduced within a common framework, while also enabling previously unexplored configurations and new methodologies to be easily implemented and evaluated. Using TestBED, we further conduct a large-scale benchmark of contemporary BED methods, including controlled studies of under-appreciated but surprisingly critical methodological choices, such as the importance of time embeddings in design policy architectures. By providing a modular, extensible software foundation and a reproducible benchmarking suite, we hope that TestBED will accelerate methodological research in BED and lower the barrier to its real-world application.

stat.ML · cs.LG

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新论文 · v1arXiv:2611.06631

Trick-or-Treat: Adversarial Evaluation of Multimodal Medical Reasoning with Synthetic Images

Oishi Banerjee, Eujin Lee, Sung Eun Kim, Alexandra N. Willauer, Christine Y. Zhou, Zhizhou Yang, Pranav Rajpurkar

原文摘要

We used medical image synthesis to create a challenging multimodal evaluation framework that exposes hallucination behaviors in frontier models. Evaluating multimodal reasoning has historically been difficult because existing medical datasets rarely pair different image modalities, such as radiographs and external clinical photographs. To address this gap, we introduce Trick-or-Treat, an adversarial multimodal medical benchmark that pairs real chest radiographs with synthetic clinical photographs to challenge models' cross-modal reasoning. For each of a wide range of conditions, we created two pairs of images, where the pairs share a single chest radiograph but contain different external photographs. In matched pairs, the chest radiograph and photograph both contain findings suggesting the same disease (e.g., varicella pneumonia suggested by lung opacities and crusted skin lesions seen in chickenpox). In mismatched pairs, the photograph depicts an alternative clinical finding that would not lead to the same unifying diagnosis (e.g. replacing crusted skin lesions with ordinary acne). Synthetic photograph generation allows clinical findings to be varied while the person's features and photographic setting stay consistent, reducing confounding. In an investigation of three frontier models, we found that models hallucinated clinically significant photograph findings in 45-57.5% of mismatched pairs, up from 25-37.5% for the same photographs shown alone. When faced with mismatched pairs, models tended to ignore mismatches and instead hallucinated findings that better matched the radiograph (e.g. misreading acne as crusted skin lesions to justify diagnosing chickenpox). Trick-or-Treat both demonstrates concerning error pattern in frontier models' medical reasoning and provides a proof of concept for the use of synthetic images in adversarial multimodal evaluation.

cs.CV · cs.AI

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新论文 · v1arXiv:2611.06630

Noise-regularized training of quantum circuit Born machines under a finite shot budget

Hoang M. Ngo, Quan Nguyen, Jung Taek Seo, Wanli Xing, My T. Thai

原文摘要

Quantum circuit Born machines (QCBMs) can learn distributions that are hard to sample from classically, but their value depends on generalizing from finite training data. Here, we study how hardware noise affects this generalization. We show that, for any noise process, the generalization gap of a QCBM trained with the maximum mean discrepancy is bounded by the complexity of the distributions the noisy model can reach. Scrambled local Pauli noise contracts this complexity but biases the model towards the uniform distribution, so residual noise acts as a regularizer whose optimal strength can be intermediate. We propose GENIE, a framework that realizes a chosen noise level with capped error mitigation and selects it during training from held-out data within a fixed execution budget, with provably bounded regret. In simulations on synthetic and census data, GENIE stays close to the best of four noise-control strategies, whereas every fixed noise level falls short in many settings.

quant-ph · cs.AI · cs.LG

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新论文 · v1arXiv:2611.06629

Does Frontier AI Strengthen Cyber Defense? Evidence from Project Glasswing

Thomas Mosk

原文摘要

Defenders and attackers race to find the same software flaws. I use the launch of Project Glasswing, which gave a small group of large US tech firms early access to Claude Mythos Preview, a model for finding software vulnerabilities, to study how access to frontier AI affects defenders, external researchers, and attackers. Using the universe of publicly disclosed vulnerabilities, I find that firms with access more than doubled their disclosures relative to Chinese and Russian tech firms without access, disclosed about 150 percent more high-impact vulnerabilities, and fixed reported flaws as quickly as before. As treated firms found more flaws themselves, external researchers, who sell their findings through bug bounties, reported about half as many vulnerabilities in treated firms' code and more in other firms' code, without leaving the market. There is no evidence that attackers exploited the additional vulnerabilities: the share exploited within a month of disclosure fell by about two thirds, mainly because a smaller share was already exploited when disclosed, while the number exploited did not change. Frontier AI helped defenders find and fix more flaws before attackers exploited them, but its restricted release creates an asymmetry in software security between firms in different countries.

econ.GN · cs.AI

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新论文 · v1arXiv:2611.06628

From Certified Pairs to a Certified Hub: Reasoner-Checked Crosswalks for the Information Exchange Standard

Fabio Rovai

原文摘要

A national digital twin was conceived as an ecosystem of twins connected through mappings to a common foundation, but the roadmap that promises to confirm those mappings are valid never says what valid means. We give the test a precise form, coherence and conservativity of the merged ontology, borrowed from logic-based alignment repair, and apply it to the Information Exchange Standard (IES), first pair by pair and then to whole hubs. Crosswalks from IES into thirteen ontologies across seven sector groups, anchored on the building ontologies BOT, Brick, and SAREF4BLDG, pass through separate stages of matching, human review, certification of each SKOS correspondence against the two class hierarchies, and reasoning with HermiT. Pair by pair, the stages catch different failures: certification alone removed all 526 conservativity violations of the raw crosswalks, review alone left 73, and together they made eleven of twelve overlapping pairs coherent and conservative. Pairwise certification does not compose. Merging the three certified building crosswalks into one hub made five Brick classes unsatisfiable, through a homonym between the IES and Brick notions of a floor that only BOT's disjointness axioms expose; rejecting that one row gave a coherent and conservative hub that entails 290 cross-ontology links nobody wrote down. The core hub is inconsistent before any crosswalk is added, because two published standards, the Open Energy Ontology and BFO 2020, contradict each other; with either one left out it is coherent and conservative. A hub, not a pair, is the unit an ecosystem of twins has to certify.

cs.AI · cs.DB · cs.LO

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